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Genetic Programming (GP) is a popular and powerful evolutionary optimization algorithm that has a wide range of applications such as symbolic regression, classification and program synthesis. However, existing GPs often ignore the intrinsic structure of the ground truth equation of the symbolic regression problem. To improve the search efficacy of GP on symbolic regression problems by fully exploiting the intrinsic structure information, this paper proposes a genetic programming with separability detection technique (SD-GP). In the proposed SD-GP, a separability detection method is proposed to detect additive separable characteristics of input features from the observed data. Then based on the separability detection results, a chromosome representation is proposed, which utilizes multiple sub chromosomes to represent the final solution. Some sub chromosomes are used to construct separable sub …